Assessing Environmental Effects upon Modeling the Individual Lactation Curve of a Local Goat Population in a Pastoral System.

The present study aims to use Wood's model to determine the parameters of individual lactation curves in a local goat population and their factors of variation under a pastoral system. A total of 137,927 records from 432 local goats were collected to assess the impact of litter size, year and month of kidding, herd and the age of the dam on lactation curve parameters. Wood's model parameters were estimated using non-linear regression, and individual curves were fitted. The characteristics of the lactation curves were computed. The initial yield (A), rate of increase (B) and rate of decline (C) parameters in Wood's model for local goats were 730 g, 0.26 and 0.09 respectively. The values of peak milk production (PP), peak date (PD) and persistency (PC) were 931.88 g/d, 23.39 days and 91.50%, respectively. Persistence was higher in goats with simple births, while peak production increased by around 0.3 kg for each additional kid. The curve parameters "A" and "C" differed according to the herd and month of kidding (p < 0.05); the age of the dam only had an impact on parameter "A" (p < 0.01). Parameter "B" was not significantly influenced by any of the factors considered (p > 0.05). Correlation coefficients among lactation curve characteristics were ranged from -0.20 to 0.89. Due to a significant negative correlation, selecting for parameter "A" may have an adverse effect on parameter "B", resulting in a shorter time to reach peak production and less persistency, but an increase in peak production among goats. The curves derived from Wood's model suggest that the shape of the curve may serve as a basis for herd management planning and to improve local population potentialities.

On the modelling of weights of kids to enhance growth in a local goat population under Tunisian arid conditions: the maternal effects.

To provide the local goat population with an adequate breeding scheme under an arid environment, this study aimed to evaluate the use of alternative models attempting to dissect the additive genetic (AG) and permanent environmental (PE) components of direct and maternal effects on weights of kids up to weaning. Records of 903 local kids over a period of 16 years were used in this study. Data were split into four groups corresponding to four periods along weight recording. Periods 1/2/3/4 contained weights in an interval with upper-lower limits of 1-20/25-60/65-120/125-150 days of age. Models including or ignoring maternal genetic or permanent environmental effects were fitted for all traits. For all periods, the best models were those including the AG component for both direct and maternal effects and the direct PE effect. Heritability estimates of both the direct and maternal effects ranged from low (0.02 for maternal heritability in P1) to moderate (0.17 for direct and maternal heritability in P2 and P3). Period 1 showed the lowest values for heritability of both direct and maternal effects, with also the largest estimate of the ratio of residual to total variance (around 0.2) compared with the other periods, with decreasing ratios as age increased (from 0.13 for P2 to 0.07 for P4). Both direct and maternal estimated breeding values (EBVs) showed high correlations for models fitting direct AG and PE (DGP) effects. For direct EBVs (DEBVs), correlations were above 0.99, indicating that the same animals are expected to be selected under any model that includes those components, regardless of the maternal effects included. For maternal EBVs, correlations were also high, but slightly lower than for the DEBVs between models including DGP effects and maternal genetic effects. Overall, our recommendation for genetic evaluations of direct and maternal effects in this population raised in extensive and harsh conditions is to use weight records preferably collected during the period of high milk production of dams, for which direct and maternal effects are expected to show full expression. Complete pedigrees and several generations of dam-progeny recording are needed to obtain a proper separation of environmental and genetic components.

Breeding Strategies for Weather Resilience in Small Ruminants in Atlantic and Mediterranean Climates.

Many efforts are being made to cope with negative consequences of climate change (CC) on livestock. Among them, selective breeding of resilient animals to CC is presented as an opportunity to maintain high levels of performance regardless of variation in weather. In the present work, we proposed a set of breeding strategies to improve weather resilience in dairy goats raised in north-western European Atlantic conditions and dairy sheep raised in Mediterranean conditions while improving production efficiency at the same time. Breeding strategies differed in the selection emphasis placed on resilience traits, ranging from 0 to 40% in the index. Simulations were carried out mimicking real breeding programs including: milk yield, length of productive life, age at first kidding and mastitis incidence in dairy goats and milk, fat and protein yields, and fertility for dairy sheep. Considering the particular climatic conditions in the two regions, the predicted future climate scenarios, and genetic correlations among breeding objectives, resilience was defined as stability to weather changes for dairy goats and as the ability to improve performance under heat stress for dairy sheep. A strategy giving a selection weight of 10 and 20% for goat and sheep resilience, respectively, resulted in the best overall genetic response in terms of both, production and resilience ability. Not considering resilience in breeding programs could lead to a major production loss in future climate scenarios, whereas putting too much emphasis on resilience would result in a limited progress in milk production.

Understanding the seasonality of performance resilience to climate volatility in Mediterranean dairy sheep.

As future climate challenges become increasingly evident, enhancing performance resilience of farm animals may contribute to mitigation against adverse weather and seasonal variation, and underpin livestock farming sustainability. In the present study, we develop novel seasonal resilience phenotypes reflecting milk production changes to fluctuating weather. We evaluate the impact of calendar season (autumn, winter and spring) on animal performance resilience by analysing 420,534 milk records of 36,908 milking ewes of the Chios breed together with relevant meteorological data from eastern Mediterranean. We reveal substantial seasonal effects on resilience and significant heritable trait variation (h2 = 0.03-0.17). Resilience to cold weather (10 °C) of animals that start producing milk in spring was under different genetic control compared to autumn and winter as exemplified by negative genetic correlations (- 0.09 to - 0.27). Animal resilience to hot weather (25 °C) was partially under the same genetic control with genetic correlations between seasons ranging from 0.43 to 0.86. We report both favourable and antagonistic associations between animal resilience and lifetime milk production, depending on calendar season and the desirable direction of genetic selection. Concluding, we emphasise on seasonal adaptation of animals to climate and the need to incorporate the novel seasonal traits in future selective breeding programmes.

Genetic analysis of live weight of local kids to promote genetic evaluations in the arid areas of Tunisia.

Goat meat production, a widely extended activity in the more arid areas of Tunisia, relies on local breeds. These breeds are well adapted to produce under harsh conditions but have a very small size and low productivity. The aim of this study was to establish the basis for future genetic evaluations to improve growth potential of this local stock. A total of 13,095 body weights and pedigree of 945 kids in the caprine herd of the Arid Areas Institute of Médenine were used. Random regression (RR) and multiple trait (MT) models were analyzed and compared. All models included effects of age and weight of dam, age, sex and type of birth of the kid, and year × month of recording, plus random direct and maternal additive genetic and permanent environmental effects. RR and MT models behave similarly, with RR showing slightly better goodness of fit. Heritability estimates for direct (ranging from 0.15 to 0.4) and maternal (0.05 to 0.3) effects showed that efficient selection for weight is feasible in this population. Estimated correlations between ages were high (> 0.8) for direct effects across all ages and low (down to 0.2) for weights taken at distant ages for maternal effects. Estimated genetic correlations between direct and maternal components revealed an antagonistic relationship, especially at early ages. Recording of at least one weight in the first month of age of the kids to evaluate the maternal capacity and a later weight to evaluate direct effects on weight is recommended for genetic evaluations under field conditions.

Joint genomic evaluation of French dairy cattle breeds using multiple-trait models.

BACKGROUND: Using a multi-breed reference population might be a way of increasing the accuracy of genomic breeding values in small breeds. Models involving mixed-breed data do not take into account the fact that marker effects may differ among breeds. This study was aimed at investigating the impact on accuracy of increasing the number of genotyped candidates in the training set by using a multi-breed reference population, in contrast to single-breed genomic evaluations. METHODS: Three traits (milk production, fat content and female fertility) were analyzed by genomic mixed linear models and Bayesian methodology. Three breeds of French dairy cattle were used: Holstein, Montbéliarde and Normande with 2976, 950 and 970 bulls in the training population, respectively and 964, 222 and 248 bulls in the validation population, respectively. All animals were genotyped with the Illumina Bovine SNP50 array. Accuracy of genomic breeding values was evaluated under three scenarios for the correlation of genomic breeding values between breeds (r(g)): uncorrelated (1), r(g) = 0; estimated r(g) (2); high, r(g) = 0.95 (3). Accuracy and bias of predictions obtained in the validation population with the multi-breed training set were assessed by the coefficient of determination (R(2)) and by the regression coefficient of daughter yield deviations of validation bulls on their predicted genomic breeding values, respectively. RESULTS: The genetic variation captured by the markers for each trait was similar to that estimated for routine pedigree-based genetic evaluation. Posterior means for rg ranged from -0.01 for fertility between Montbéliarde and Normande to 0.79 for milk yield between Montbéliarde and Holstein. Differences in R(2) between the three scenarios were notable only for fat content in the Montbéliarde breed: from 0.27 in scenario (1) to 0.33 in scenarios (2) and (3). Accuracies for fertility were lower than for other traits. CONCLUSIONS: Using a multi-breed reference population resulted in small or no increases in accuracy. Only the breed with a small data set and large genetic correlation with the breed with a large data set showed increased accuracy for the traits with moderate (milk) to high (fat content) heritability. No benefit was observed for fertility, a lowly heritable trait.

Muscle-specific gene expression is underscored by differential stressor responses and coexpression changes.

Variations on the transcriptome from one skeletal muscle type to another still remain unknown. The reliable identification of stable gene coexpression networks is essential to unravel gene functions and define biological processes. The differential expression of two distinct muscles, M. flexor digitorum (FD) and M. psoas major (PM), was studied using microarrays in cattle to illustrate muscle-specific transcription patterns and to quantify changes in connectivity regarding the expected gene coexpression pattern. A total of 206 genes were differentially expressed (DE), 94 upregulated in PM and 112 in FD. The distribution of DE genes in pathways and biological functions was explored in the context of system biology. Global interactomes for genes of interest were predicted. Fast/slow twitch genes, genes coding for extracellular matrix, ribosomal and heat shock proteins, and fatty acid uptake centred the specific gene expression patterns per muscle. Genes involved in repairing mechanisms, such as ribosomal and heat shock proteins, suggested a differential ability of muscles to react to similar stressing factors, acting preferentially in slow twitch muscles. Muscle attributes do not seem to be completely explained by the muscle fibre composition. Changes in connectivity accounted for 24% of significant correlations between DE genes. Genes changing their connectivity mostly seem to contribute to the main differential attributes that characterize each specific muscle type. These results underscore the unique flexibility of skeletal muscle where a substantial set of genes are able to change their behavior depending on the circumstances.